title: Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.
abstract: Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.
authors.affiliation: Department of Pharmacology, Faculty of Pharmacy, Mahidol University, 447 Sri-Ayuthaya Road, Rajathevi, Bangkok 10400, Thailand.
authors.forename: Anunchai
authors.initials: A
authors.lastname: Assawamakin
authors.forename: Supakit
authors.initials: S
authors.lastname: Prueksaaroon
authors.forename: Supasak
authors.initials: S
authors.lastname: Kulawonganunchai
authors.forename: Philip James
authors.initials: PJ
authors.lastname: Shaw
authors.forename: Vara
authors.initials: V
authors.lastname: Varavithya
authors.forename: Taneth
authors.initials: T
authors.lastname: Ruangrajitpakorn
authors.forename: Sissades
authors.initials: S
authors.lastname: Tongsima
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015415:Biomarkers; D020869:Gene Expression Profiling; D006801:Humans; D046228:Microarray Analysis / Q000706:statistics & numerical data*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D040901:Proteomics / Q000379:methods / Q000706:statistics & numerical data*
chemical_list: D015415:Biomarkers